Information Criteria Determine the Number of Active Sources

With the neuroelectromagnetic inverse problem, the optimal choice of the number of sources is a difficult problem, especially in the presence of correlated noise. In this paper we present a number of information criteria that help to solve this problem. They are based on the probability density function of the measurements or their eigenvalues. make use of the Akaikc or MDL (minimum description Icngth) correction term and all employ sonic sort of noisc information. By esteiisivc simulations we invcstigatcd the conditions under which these criteria yield rcliable estimations. We were able to quantify two major factors of influence: (1) the precision of tlie noise information and (2) the signal-to-noise ratio (SNR). here defined as the ratio of the smallcst signal eigenvalues and the average of the noise eigenvalues. Furthermore. we found that the Akaikc correction term tends to overestimate. duc to its greater sensibility to the precision of the noise information.